Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Open-Loop Control on the Efficiency of Quantum Battery With Reservoir Engineering(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2025-06-28) Borisenok, S.We discuss the case of a harmonic oscillator-based quantum battery strongly coupled to a highly nonMarkovian thermal reservoir via the quantum charger described by the Caldeira–Leggett model. The coupling between the reservoir and the battery serves as a control parameter for the system. We consider the system to stay in the strongly underdamped regime. Within the framework of the open-loop approach, we determine the optimal shape of control for the battery charging work and then restore the control coupling characteristics of QB in the Hamiltonian for the alternative cases of low and high temperatures. Ultimately, we discuss some possible ways to develop our model for the feedback algorithms. © 2025 Elsevier B.V., All rights reserved.Article On Feedback Control Algorithms for Nitrogen-Vacancy Quantum Sensing(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024-11-30) Borisenok, S.Ultrasensitive quantum detection of external weak signals at the nanoscale levels can be implemented in a variety of forms. Here we discuss different feedback control algorithms for the sensing scenario based on the semiclassical Tavis-Cummings model for nitrogen-vacancy (NV) centers located in the diamond. In the frame of this model, the sensing elements are considered as non-interacting two-level quantum systems, distributed in-homogeneously due to heterogeneous local magnetic and strain environments. The dynamical system of ordinary differential equations corresponding to the model contains the set of control parameters: the detunings between the drive frequency and the cavity frequency and between the drive frequency and NV transition frequency, as well as the relaxation coefficients. Correspondingly, it opens a gate for developing feedback control algorithms for tracking the cavity field, the income signal, and the reflection signal in the model sensing system. To study the principal features of algorithmic feedback we formulate the simplified ’toy model’ for the TavisCummings system and investigate alternative schemes of feedback (gradient methods, target attractor methods) to compare their pros and cons for effective control for nitrogen-vacancy-cavity quantum sensing based on different choices of the control parameter set. This work was supported by the Research Fund of Abdullah Gül University; Project Number: BAP FBA-2023-176 ’Geribesleme kontrol algoritmaları ile kubit tabanlı sensörlerin verimliliğinin artırılması’. The paper was presented at PhysCon2024. © 2024 Elsevier B.V., All rights reserved.Article Control Over the Training Performance of Quantum State Tomography With Reservoir Computing Networks(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024-12-28) Borisenok, S.The evaluation of unknown states for a given quantum system is one of the key problems in quantum information processing. The most efficient method of state characterization is quantum state tomography (QST), where the full-density matrices are reconstructed from the experimental measurements or numerical simulations performed on quantum states. The improvement of the computational performance in quantum state tomography and its related problems is a challenging task for modern theoretical physics. The general scheme of computing deals with the input information that goes into a quantum reservoir through a recurrent evolution. After the evolution, the final output is obtained as the linear combination of the readout elements. In our approach, the quantum reservoir is modeled with the Lindbladian equation. The control over performance is made by the coherent coupling parameter between the input quantum state and the reservoir. The control feedback algorithm is represented with the set of Kolesnikov’s target attractor algorithm to drive certain parameters of quantum state tomography, particularly, the outputs for the density matrix. Here we formulate the target attractor feedback in a discrete form to improve the training performance of QST and then develop a basic example of the state tomography for the quantum system of spin 1/2. We conclude by mentioning the basic features of our algorithm and its possible development. © 2025 Elsevier B.V., All rights reserved.